being pursued online: applying cyberlifestyle-routine activities theory to cyberstalking...

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2011 38: 1149Criminal Justice and BehaviorBradford W. Reyns, Billy Henson and Bonnie S. Fisher

Cyberstalking VictimizationRoutine Activities Theory to−Being Pursued Online : Applying Cyberlifestyle

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1149

CRIMINAL JUSTICE AND BEHAVIOR, Vol. 38 No. 11, November 2011 1149-1169DOI: 10.1177/0093854811421448© 2011 International Association for Correctional and Forensic Psychology

AUTHORS’ NOTE: The authors would like to thank Professors Francis Cullen, Chris Schreck, and Pamela Wilcox for their insightful comments on an earlier version of this article. Please direct all correspondence to Brad Reyns, Weber State University, Department of Criminal Justice, 1206 University Circle, Ogden, UT 84408–1206; email: [email protected].

BEING PURSUED ONLINE

Applying Cyberlifestyle–Routine Activities Theory to Cyberstalking Victimization

BRADFORD W. REYNSWeber State University

BILLY HENSONShippensburg University

BONNIE S. FISHERUniversity of Cincinnati

Building upon Eck and Clarke’s (2003) ideas for explaining crimes in which there is no face-to-face contact between victims and offenders, the authors developed an adapted lifestyle–routine activities theory. Traditional conceptions of place-based environments depend on the convergence of victims and offenders in time and physical space to explain opportunities for victimization. With their proposed cyberlifestyle–routine activities theory, the authors moved beyond this conceptualization to explain opportunities for victimization in cyberspace environments where traditional conceptions of time and space are less relevant. Cyberlifestyle–routine activities theory was tested using a sample of 974 college students on a particular type of cybervictimization—cyberstalking. The study’s findings provide support for the adapted theoretical perspective. Specifically, variables measuring online exposure to risk, online proximity to motivated offenders, online guardianship, online target attractiveness, and online deviance were significant predictors of cyberstalking victimization. Implications for advancing cyberlifestyle–routine activities theory are discussed.

Keywords: routine activities; lifestyles; cyberstalking; victimization; cybercrime

The Internet has transformed the daily routines of millions of individuals around the world, and accessing the Internet has become increasingly important in many facets of

life, especially communication and social interaction. With the Internet’s global span, a seemingly infinite number of opportunities to meet new people, develop personal and professional networks, and encounter new social situations is available almost instantaneously. These opportunities exist at all hours of the day and night, far outnumbering comparable offline opportunities. The Internet also provides a new venue for criminal activities, such as cyberstalking, and with the vast volume of users, an almost endless supply of potential victims.

Explaining opportunities for victimization, especially the application and testing of the lifestyle–routine activities approach, is a central focus of the field of victimology. In recent years, the theoretical perspective has been applied to a number of types of victimization



1150 CRIMINAL JUSTICE AND BEHAVIOR

such as stalking (Fisher, Cullen, & Turner, 2002; Mustaine & Tewksbury, 1999), sexual assault (Cass, 2007; Mustaine & Tewksbury, 2002; Schwartz & Pitts, 1995), property crime (Fisher, Sloan, Cullen, & Lu, 1998; Sampson & Wooldredge, 1987; Wilcox Rountree, Land, & Miethe, 1994), and online forms of victimization (Choi, 2008; Holt & Bossler, 2009; Holtfreter, Reisig, & Pratt, 2008; Pratt, Holtfreter, & Reisig, 2010). The underlying premise of this approach is that a victim and offender must converge physically in time and space to produce an elevated risk of victimization. However, contrary to this premise, the physical intersection in time and space of the victim and offender is not a necessary element in creating opportunities for online victimization. Yet other elements posited by lifestyle–routine activities theory—exposure, proximity to potential offenders, guardianship, and target attractiveness—are conducive to creating opportunities for victimization in cyberspace environments.

Researchers have examined different aspects of cybervictimization (e.g., online fraud, online harassment, sexual solicitation, cyberstalking), mostly with an eye toward describing and estimating the prevalence and incidence of victimization (e.g., Finkelhor, Mitchell, & Wolak, 2000; Finn, 2004; Nhan, Kinkade, & Burns, 2009; Spitzberg & Hoobler, 2002; Wolak, Mitchell, & Finkelhor, 2007). A limited number of researchers have attempted to utilize the lifestyle–routine activities perspective to explain opportunities for online victimization (Bossler & Holt, 2009; Choi, 2008; Holt & Bossler, 2009; Holtfreter et al., 2008; Marcum, Higgins, & Ricketts, 2010; Pratt et al., 2010). Thus far, however, the issue of separation in time and space between the victim and offender and how this divergence affects the opportunity structure for online victimization has not been addressed theoretically. Adapting lifestyle–routine activities theory to take into account the spatial and temporal divergence between victims and offenders in cyberspace environments has implications not only for the theory itself but also for the operationalization and measurement of the key lifestyle–routine activities concepts. The current study addresses these theoretical and empirical shortfalls by examining online lifestyles and routine activities that might create opportunities that can put individuals at risk for a particular type of cybervictimization—cyberstalking.

LIFESTYLE–ROUTINE ACTIVITIES THEORY: A DIVERGENCE IN TIME AND SPACE?

Lifestyle-exposure theory and routine activities theory developed in tandem. Over time, common theoretical assumptions have led to an implicit fusion of the two theories into a theoretical perspective often referred to as lifestyle–routine activities theory (Cohen & Felson, 1979; Garofalo, 1987; Hindelang, Gottfredson, & Garofalo, 1978). Lifestyle–routine activities theory provides an explanation of how opportunities for criminal victimization are produced by individuals’ everyday routines and lifestyle behaviors that expose them to risk (Felson, 2002; Hindelang et al., 1978). Its central premise is the intersection between motivated offenders and suitable targets (i.e., victims) in the absence of capable guardians that creates opportunities for victimization (Cohen & Felson, 1979). Originally, lifestyle-exposure theory and routine activity theory assumed that this interaction would be a direct physical encounter, which by necessity occurs in a physical environment (i.e., a place) occupied by both the victim and the offender.

The linkage between crime and place has long been of interest to criminologists (Brantingham & Brantingham, 1995; Eck & Weisburd, 1995; Shaw & McKay, 1942;



Reyns et al. / BEING PURSUED ONLINE 1151

Sherman, Gartin, & Buerger, 1989). However, researchers have comparatively ignored crimes that do not occur at a physical location. When lifestyle-exposure theory and routine activities theory were originally proposed, cyberspace as we know it today did not exist, and only physical encounters between victims and offenders were considered a necessary condition to create an opportunity for victimization. In cyberspace, however, this is not the case. Potential victims and would-be offenders can come together without any interaction in the same physical space, leading many to speculate as to the usefulness of the theories in explaining victimization that occurs in a cyberspace environment. Furthermore, whereas some scholars have speculated as to the applicability of the theory, others have taken for granted the theoretical premise of an intersection in time and space and assumed that the theory should apply to cybercrimes without explaining why it should apply (e.g., Marcum, 2009; Marcum et al., 2010; Pratt et al., 2010).

Convincing arguments have been put forth for each side of the debate as to whether the components of lifestyle–routine activities theory can be adapted to explain victimization in cyberspace environments. For instance, Grabosky (2001) contends

One of the basic tenets of criminology holds that crime can be explained by three factors: motivation, opportunity, and the absence of a capable guardian. This explanation can apply to an individual incident as well as to long-term trends. Derived initially to explain conventional “street” crime, it is equally applicable to crime in cyberspace. (p. 248)

On the other hand, Yar (2005) has argued that the components of lifestyle–routine activities theory are not suitable for application to cybercrimes, explaining that

the routine activity theory holds that the “organization of time and space is central” for criminological explanation (Felson 1998: 148), yet the cyber-spatial environment is chronically spatio-temporally disorganized. The inability to transpose RAT’s [routine activities theory’s] postulation of “convergence in time and space” into cyberspace thereby renders problematic its straightforward explanatory application to the genesis of cybercrimes. (p. 424)

In other words, Yar’s (2005) main argument against the application of lifestyle–routine activities theory to cybercrime is that the victim and offender must intersect in time and space, which is not the case in cyberspace. For example, most types of online fraud involve the offender somehow contacting the victim, usually by sending an e-mail, and convincing the victim to send money or personal information (e.g., bank account number, password) for some specified reason. With these types of incidents, the victim and offender do not come together in the same physical place (in reality, they are most likely in different locations, such as cities or even countries), and most often they are not communicating in real time; hence, a divergence in time and space exists. These inconsistencies can be reconciled by adapting lifestyle–routine activities theory to explain victimization in cyberspace environments.

Tillyer and Eck (2009) have argued that “either routine activities theory is limited to place-based crimes or it needs revision” (p. 286). Given technological changes previously described that have created new opportunities for victimization, the next logical step is to develop a revised conceptualization of lifestyle–routine activities theory that takes into account the divergence in time and space between victims and offenders. To do so, it is first necessary to conceive of victimization in cyberspace as what Eck and Clarke (2003) have




termed “systems problems” (p. 35). That is, there are a number of crimes in which the victim and offender do not come face to face or are not in the same physical location but instead interact through a system or network, such as a telecommunications system or a mail/parcel delivery system. From this perspective, the divergence in space between victims and offenders can be reconceived to explain opportunities for cybervictimization. That is to say, although victims and offenders involved in cybercrimes do not converge in the traditional sense in physical space, they do come together within a system of networked devices, such as cyberspace.

Although the theoretical approach discussed above describes the transition from places to networks, it does not address the issue of the divergence in time between victims and offenders in cyberspace environments. Although interactions between victims and offenders in physical space happen in real time, this is not always the case with cybercrimes. For example, a cyberstalker may send an e-mail to a victim in the morning, say at 9 a.m. (Time 1). However, the victim may not be online at that exact time to receive the message, creating a time lag between the cyberstalker’s original actions and the victim’s receipt of the e-mail. In this scenario, although the e-mail may not have been received, it is waiting, and when the victim checks his or her e-mail, the offender’s e-mail and the victim will intersect at some time after 9 a.m. (Time 2). The victim and offender did not converge in the traditional physical sense in time and space (i.e., face to face). Instead, the offender acted and waited for the victim to experience the intent of the act. At that time, when the victim opened and read the e-mail, the cyberstalking victimization incident transpired. This example involves the asynchronous intersection in time and space of the victim and the offender; their convergence is contingent on (a) the network providing a conduit for interaction between victim and offender, with cyberspace acting as a proxy for physical space, and (b) an eventual overlap in time or a completed transaction across time.

Holt and Bossler (2009) have pointed out that without empirical examinations of the issue of the applicability of the theory to cybercrimes, any conclusions about the effectiveness of lifestyle–routine activities theory in explaining victimization are premature. The current study uses this adapted lifestyle–routine activities approach to empirically examine cyberstalking victimization. In doing so, we expand the theoretical development of lifestyle–routine activities theory to online victimization in a cyberspace environment, specifically testing what we refer to as cyberlifestyle–routine activities theory.

CIRCUMSTANCES THAT CREATE OPPORTUNITIES FOR VICTIMIZATION

An intersection in time and space of potential victims and offenders may not be all that is necessary to create an opportunity for victimization within a cyberspace environment. In testing lifestyle–routine activities theory, researchers have identified exposure to motivated offenders, proximity to motivated offenders, target attractiveness, and guardianship as key causal mechanisms in explaining opportunities for victimization. All else equal, greater exposure to motivated offenders, greater proximity to motivated offenders, and greater target attractiveness are hypothesized to increase risks for victimization. Guardianship, on the other hand, acts as a buffer against victimization by disrupting criminal opportunity structures, thereby decreasing likelihood of victimization. These theoretical propositions have received strong empirical support in accounting for a variety of types of personal and property victimization across different samples (e.g., Cohen, Felson, & Land, 1980; Cohen,




Kluegel, & Land, 1981; Fisher, Daigle, & Cullen, 2010; Fisher et al., 1998; Holtfreter et al., 2008; Miethe & Meier, 1990; Mustaine & Tewksbury, 1999; Pratt et al., 2010; Schreck & Fisher, 2004; Schreck, Wright, & Miller, 2002; Spano & Freilich, 2009; Wilcox Rountree et al., 1994).

Researchers have applied lifestyle–routine activities theory to online forms of victimization in only a handful of studies. Results indicate that the core theoretical constructs of exposure and guardianship have inconsistent effects across these studies, possibly due to differences in conceptualization or measurement of the theoretical concepts or examination of different dependent variables. For example, in examining sexual solicitation of college freshmen online, Marcum (2009) reported that exposure and guardianship (e.g., having strangers present in the room at the time of Internet use) increased victimization risk. However, Choi (2008) reported that digital guardianship (e.g., presence of security software programs) decreased the likelihood of computer virus victimization among college students. He concluded that an online lifestyle characterized by activities such as spending time online and engaging in online deviance such as media piracy increased student risk of virus victimization. The finding that participation in online deviance increases risk for cybervictimization has been echoed by other researchers. Holt and Bossler (2009), for instance, reported that risky online activities, particularly engaging in computer hacking, increased college students’ risks for online harassment (see also Bossler & Holt, 2009).

Overall, the few studies that have examined cybervictimization from a lifestyle–routine activities perspective suggest that engaging in risky or deviant online activities puts individuals at increased risk for victimization, as does online exposure, while guardianship has not performed reliably. The effects of target attractiveness and online proximity to motivated offenders on cybervictimization have thus far not been examined empirically. More work is needed with respect to both adapting these theoretical concepts to cyberspace and estimating the separate effect of each on cybervictimization while controlling for their combined effects. Since the current study focuses on cyberstalking to test this adapted cyberlifestyle–routine activities theory, a discussion of previous cyberstalking victimization studies follows.

CYBERSTALKING VICTIMIZATION

Cyberstalking can be defined as the repeated pursuit of an individual using electronic or Internet-capable devices (Reyns, Henson, & Fisher, in press). A number of online behaviors on the part of the offender can be considered cyberstalking. According to the National Crime Victimization Study stalking supplement, these behaviors include harassment or threats via e-mail, instant messenger, chat rooms, message or bulletin boards, or other Internet sites (Baum, Catalano, Rand, & Rose, 2009). Cyberstalkers can also use electronic devices to monitor their victims, such as cameras, listening devices, computer programs, and Global Positioning System. Although there is by no means a widely agreed-upon definition of cyberstalking, the above definition is compatible with most of the extant research on the topic, emphasizing repeated pursuit behaviors by electronic means (D’Ovidio & Doyle, 2003; Finkelhor et al., 2000; Finn, 2004; Holt & Bossler, 2009; Jerin & Dolinsky, 2001; Marcum et al., 2010; Sheridan & Grant, 2007; Spitzberg & Hoobler, 2002).

Despite the apparent interest in the topic of cyberstalking victimization by researchers (e.g., Parsons-Pollard & Moriarty, 2008; Pittaro, 2007; Reyns, 2010; Roberts, 2008) and




government officials (Ashcroft, 2001; Reno, 1999), few empirical assessments of this type of victimization have been undertaken (Parsons-Pollard & Moriarty, 2009). The studies that have examined cyberstalking victimization suggest that it is a potentially widespread event that is worthy of further study. For instance, Reyns et al. (in press) estimated the lifetime prevalence of cyberstalking victimization among a sample of college undergraduates in the Midwest to be 40.8%, with more than 46% of the women and more than 32% of the men experiencing online pursuit behaviors. Earlier studies of cyberstalking support the finding that this type of victimization is experienced by a significant portion of individuals (e.g., Alexy, Burgess, Baker, & Smoyak, 2005; Baum et al., 2009; D’Ovidio & Doyle, 2003; Fisher et al., 2002; Jerin & Dolinsky, 2001; Sheridan & Grant, 2007; Spitzberg & Hoobler, 2002). For example, in the stalking supplement to the National Crime Victimization Study, it was reported that among victims of stalking, 26% also experienced some form of cyberstalking as part of the pursuit behaviors of the offender (Baum et al., 2009).

Although a handful of research studies have been published estimating the extent of cyberstalking victimization, few have examined the factors that increase or decrease individuals’ risk for victimization. Explanations of why individuals are at risk have not, for the most part, advanced beyond descriptive demographic characteristics (e.g., sex, race, relationship status), and most studies of cyberstalking are lacking a theoretical framework. Investigating risk factors grounded in the lifestyles and routine activities of individuals that incorporate a theoretical framework allows for hypothesis testing that would advance understanding of why individuals are at risk of cyberstalking. This type of analysis also may provide insights into designing evidence-based situational crime prevention initiatives that are tailored to cyberstalking victimization. The current study addresses these theoretical and empirical limitations in the previous research in two ways. First, the current study incorporates our cyberlifestyle–routine activities theory to guide hypothesis testing and operationalization of all of the core theoretical concepts (i.e., exposure, proximity, target attractiveness, and guardianship). Second, it examines online lifestyles and routine activities within this cyberlifestyle–routine activity framework that may create online opportunities for individuals at risk for cyberstalking victimization. By addressing these issues, the current study will both expand lifestyle–routine activities theory beyond traditional conceptions of place and improve measurement of the key theoretical concepts in cyberspace environments so measures are tailored to the development of cyberstalking victimization opportunities and grounded in the cyberlifestyle–routine activities theory.

COLLEGE STUDENT VICTIMIZATION

College students have been identified as an at-risk group for a wide range of personal and property victimization experiences (Fisher, 1995; Fisher et al., 1998). Their high level of risk coupled with a propensity to routinely stay socially connected through a variety of electronic media—including online social networks, text messages, and instant messages—makes college students an ideal population for studying cyberstalking victimization (e.g., Buhi, Clayton, & Surrency, 2009; Fisher et al., 1998; Fisher et al., 2010; Jordan, Wilcox, & Pritchard, 2007). Prior research examining college student populations has focused on property victimization (e.g., Mustaine & Tewksbury, 1998), violent victimization (e.g., Fisher et al., 1998), sexual victimization (e.g., Cass, 2007; Mustaine & Tewksbury, 2002), and stalking victimization (e.g., Fisher et al., 2002; Jordan et al., 2007).




Despite researchers’ continued interest in college student victimization, the cyberstalking victimization of college students has not been extensively studied. Most of the studies that have been published have utilized nonprobability samples, such as snowball or convenience samples, or have ambiguous conceptualizations of which behaviors cyberstalking victimization entails. The current study addresses the weakness of these limited number of college student cyberstalking victimization studies. This study is the first to empirically examine the nature of cyberstalking victimization of college students from our cyberlifestyle–routine activities perspective using a large probability sample of students.

METHOD

PROCEDURE

Data were collected in spring of 2009, via a self-report victimization survey, from a simple random sample of undergraduate college students at a large urban university in the Midwest. The university registrar’s office provided the sampling frame that included students between 18 and 24 years old who were enrolled full-time during the spring term of 2009. Anticipating a low response rate due to the nature of web-based surveys (see Couper, 2000) and the youthful population under study, 10,000 students were randomly chosen for inclusion in the sample, which is approximately one third of the undergraduate student body. To help encourage students’ participation, the registrar’s office sent e-mail invitations to the selected students asking them to participate in the survey. Three waves of e-mails were sent to students’ school-issued e-mail accounts at approximately 3-week intervals. Per Dillman’s (2007) tailored-design method, the language of the invitation e-mails varied slightly across waves in an effort to elicit a higher rate of participation.

PARTICIPANTS

As is the case with most web-based surveys, the response rate for the current study is difficult to calculate because it is not possible to determine how many of the 10,000 invitations that were sent out were actually received, opened, or read by the selected students. A conservative estimate of the response rate can be calculated based on the total number of surveys completed and the total number of e-mail invitations sent. This produces a response rate of 13.1%.1 A less conservative estimate can be calculated based on the number of students who clicked on the web link to the informed consent form that was embedded in the e-mail invitation. This group of 1,951 students completed 1,310 surveys, resulting in a response rate of 67.1%. After cases containing a substantial amount of missing data were deleted, the final sample size included 974 students. If only those participants who began the survey, meaning they answered the first question (n = 1,268), were considered, the response rate based on the final sample would be 76.8% (see Pratt et al., 2010).

The final sample of students possesses the following characteristics: 61% female, 86% White, with a mean age of 20 years. The undergraduate population of students who were eligible for inclusion in the sample was 48.7% female, 80.3% White, with a mean age of 21 years. Since enrollment changes daily, these population characteristics represent the pool of eligible students as of May 18, 2009. The overall undergraduate student body for the 2008–2009 academic year was 53% female and 77% White, so the sample is slightly




heavy on females and Whites compared to their representation in the undergraduate population. Statistics related to the age of the undergraduate student body for the 2008–2009 academic year were not available.

MEASURES

Our adapted cyberlifestyle–routine activities theory guided the development of variables used in the multivariate analysis to identify risk factors for cyberstalking victimization. Descriptive statistics for each victimization, lifestyle–routine activity, and control variable are presented in Table 1.

Dependent Variables

Cyberstalking victimization. Cyberstalking is defined as the repeated pursuit of an individual using electronic or Internet-capable devices. A respondent was coded as a cyberstalking victim if he or she had been (a) repeatedly contacted online after asking the person to stop, (b) repeatedly harassed online, (c) the recipient of repeated and unwanted sexual advances, or (d) repeatedly threatened with violence while online. Respondents were asked whether they had ever experienced any of these online pursuit behaviors. These four items were used to create a fifth dependent variable that measured whether the respondent experienced one or more of these behaviors on two or more occasions. As Table 1 illustrates, 41% of respondents have experienced a form of cyberstalking at some point in their lives, with unwanted contact (23%) and harassment (20%) being the most frequently experienced types of pursuit behaviors, followed by unwanted sexual advances (14%) and threats of violence (4%).

Independent Variables

Online exposure to motivated offenders. Exposure to motivated offenders online is hypothesized to increase students’ likelihood of victimization. For crimes taking place in a physical environment, exposure has been operationalized with variables measuring time spent outside the home, time spent outside the home at night, propensity to be in at-risk environments (e.g., drinking establishments), and the like. With respect to cyberspace environments, these indicators of exposure are unlikely to increase one’s likelihood of victimization because they are indicators of exposure in physical space, not cyberspace. However, the concept of exposure can be adapted to reflect online exposure to motivated offenders.

To capture the online context of exposure to likely or motivated offenders, the current study incorporates five aspects of exposure: (a) amount of time spent online each day, (b) number of online social networks owned by the respondent, (c) the number of times each day the respondent updates his or her online social network accounts, (d) the number of photos the respondent has posted online, and (e) whether the respondent uses AOL Instant Messenger. The distribution of the number of photos measure was highly skewed, with a few respondents reporting to have 10,000 photos. As a result, the natural log of the measure is being used in the following analyses. Each of these measures represents a




degree of online exposure to motivated offenders that might facilitate the creation of opportunities for cyberstalking victimization according to the cyberlifestyle–routine activities theory.

TABLE 1: Variables, Scales, and Descriptive Statistics

Variable Scale M SD Minimum Maximum n Cronbach’s α

Dependent variable Unwanted contact (0 = no, 1 = yes) 0.23 0.42 0 1 974 Harassment (0 = no, 1 = yes) 0.20 0.40 0 1 964 Sexual advances (0 = no, 1 = yes) 0.14 0.34 0 1 953 Threats of violence (0 = no, 1 = yes) 0.04 0.20 0 1 942 Cyberstalking

victimization(0 = no, 1 = yes) 0.41 0.49 0 1 974

Independent variable Online exposure

Time spent online (Number of hours per day)

3.93 2.63 1 16 974

Number of social networks

(Number of online social network accounts)

2.56 1.65 1 15 974

Number of updates to social network

(Number of updates to social networks per day)

2.25 3.77 0 25 974

Number of photos online

(Natural log of the number of photos posted)

5.14 1.34 0 8.52 974

Use AOL Instant Messenger

(0 = no, 1 = yes) 0.35 0.47 0 1 974

Online proximity Add stranger (0 = no, 1 = yes) 0.77 0.44 0 1 974 Number of friends (Natural log of the number

of friends online)5.87 0.84 1.95 8.52 974

Friend service (0 = no, 1 = yes) 0.03 0.17 0 1 974 Online guardianship

Profile(s) set to private

(0 = no, 1 = yes) 0.82 0.37 0 1 974

Use profile tracker (0 = no, 1 = yes) 0.11 0.32 0 1 974 Deviant peers (Mean level of peer

deviance)2.09 1.52 0 10 974 .80

Online target attractiveness Composite measure (Mean level of target

attractiveness)0.58 0.23 0 1 974 .71

Gender (0 = male, 1 = female) 0.61 0.48 0 1 974 Relationship status (0 = single, 1 = nonsingle) 0.57 0.49 0 1 974 Sexual orientation (0 = heterosexual,

1 = other)0.06 0.23 0 1 974

Online deviance (Mean level of online deviance)

0.19 0.15 0 1 974 .68

Control variables Age (0 = younger than 21

years, 1 = 21 and older)0.38 0.48 0 1 974

Non-White (0 = White, 1 = non-White) 0.13 0.33 0 1 974 Offline risky activities (Mean level of offline risk) 11.61 12.65 0 75 974 .67




As Table 1 illustrates, based on the current measures of exposure, this sample of college students is connected to the Internet in a number of ways, indicating a high level of exposure. For instance, the average student in the sample is online for about 4 hours every day, and approximately 35% of the students use AOL Instant Messenger to stay connected with others.

Online proximity to motivated offenders. Proximity to motivated offenders represents the actual physical proximity of potential victims to likely offenders. However, crimes taking place within online networks do not necessarily have to involve physical or temporal proximity between the victim and the offender. Within a system such as the Internet, victims and offenders can come into virtual proximity to one another. For example, chat rooms bring together users from various physical locations (e.g., different cities, states, or countries) to participate in real-time communication within the same online forum. Were it not for the chat room, it is unlikely that these Internet users would have any interaction with one another, so participating in such a forum may reflect online proximity to motivated offenders.

In the current study, online proximity to motivated offenders is measured with three variables: (a) whether the respondent allows other Internet users whom he or she does not know (i.e., strangers) to access his or her online social networks, which may include personal information (e.g., contact information, photos, interests); (b) the number of “friends” that the respondent has across all of his or her online social networks; and (c) whether the respondent has ever utilized an online service designed to assist him or her in acquiring friends for his or her online social network. As with the number of photos measure, the distribution of the number of friends measure was highly skewed, with a few respondents reporting to have 5,000 friends. As a result, the natural log of the measure is being used in the following analyses.

Online guardianship. Prior personal victimization research has typically focused on two dimensions of guardianship: physical guardianship and social guardianship (e.g., Mustaine & Tewksbury, 1998; Newman, 1996; Sampson & Wooldredge, 1987; Tewksbury & Mustaine, 2003; Wilcox Rountree & Land, 1996). Physical guardianship often involves target hardening, such as locking doors, erecting barriers (e.g., fences), and implementing police patrols. In online environments, the physical component of guardianship has been operationalized by measuring the presence of firewalls and security programs (Choi, 2008; Holt & Bossler, 2009). However, with respect to the crime of cyberstalking, these protections are unlikely to be effective as they are designed to defend against outside threats to the computer and/or software and not protect the user against potentially unwanted communications.

Physical guardianship in cyberspace is captured in the current study with two variables: (a) whether the respondent has his or her online social network or blog set to limited access so only approved parties can view profiles/information and (b) whether the respondent utilizes an online profile tracker to view who has visited his or her social network or blog, when it was visited, and where the visitors are from. These measures are appropriate indicators of online guardianship in the current context because they might affect opportunities for cyberstalking victimization through self-guardianship. Statistics presented in Table 1 highlight the propensity toward self-guardianship among members of the sample (i.e., most students have their online social networks set to limited access).




Social guardianship indicates the presence of others who, by their presence or degree of guardianship responsibility, may discourage crimes from taking place (Felson, 1995). In terms of online/network environments, social guardianship has been measured with indicators of the victim’s online peers. For instance, Holt and Bossler (2009) operationalized a lack of social guardianship with a measure of online peer deviance. In the current study, ineffective social guardianship is similarly measured with a single variable indicating how likely the respondent believes it is that a friend will use the information he or she has posted online to harass, stalk, or threaten him or her (on a scale from 1 to 10). This variable measures a lack of guardianship online, with deviant peers (i.e., those perceived to have the potential to harass, stalk, or threaten the respondent) being less likely to serve as capable guardians.

Online target attractiveness. Target attractiveness has been described as “the material or symbolic desirability of persons or property targets to potential offenders, as well as the perceived inertia of a target against illegal treatment” (Cohen et al., 1981, p. 508). In other words, certain targets may be more attractive to a potential offender if they have some value to the offender (e.g., they are valuable or enjoyable) or if they are easy targets (e.g., small-sized items are easier to shoplift, a young or elderly person may not have the ability to resist an attack) (Clarke, 1999). In the case of online victimization, certain information might facilitate the offender’s pursuit of the victim (e.g., e-mail addresses, instant messenger IDs) or make the individual a more desirable target (e.g., posting relationship status, photos, sexual orientation), thereby increasing an individual’s attractiveness as a target.

Nine measures of target attractiveness based on the type of information the respondents posted are utilized: (a) full name, (b) relationship status, (c) sexual orientation, (d) instant messenger ID, (e) e-mail address, (f) addresses for other social network/blog sites, (g) interests and/or activities, (h) photos of themselves, and (i) videos of themselves. A single composite measure of the respondent’s target attractiveness was created by averaging these nine survey items (Cronbach’s alpha = .71). As illustrated in Table 1, based on this composite measure of online target attractiveness, the average respondent presents a moderately attractive target to potential offenders. The respondent’s gender, relationship status, and sexual orientation are also used as measures of target attractiveness in the current analysis.

Online/electronic deviant lifestyle. A large body of criminological research has identified participation in deviant activities or lifestyles as a risk factor for a variety of types of victimization (e.g., Henson, Wilcox, Reyns, & Cullen, 2010; Jensen & Brownfield, 1986; Lauritsen, Laub, & Sampson, 1992; Lauritsen, Sampson, & Laub, 1991; Sampson & Lauritsen, 1990), including cybervictimization (e.g., Choi, 2008; Holt & Bossler, 2009). Engaging in deviant lifestyles or routine activities is hypothesized to increase one’s exposure and proximity to motivated offenders (i.e., other deviants) and place the individual in situations conducive to victimization (e.g., lack of capable guardianship).

For this study, an online deviant lifestyle is measured with eight items representing different deviant acts that the respondent had previously engaged in, including whether the respondent had (a) repeatedly contacted or attempted to contact someone online after the person asked/told the respondent to stop, (b) repeatedly harassed or annoyed someone online after the person asked/told the respondent to stop, (c) repeatedly made unwanted




sexual advances toward someone, (d) repeatedly spoken to someone in a violent manner or threatened to physically harm him or her online after he or she asked/told the respondent to stop, (e) attempted to hack into someone’s online social network account, (f) downloaded music or movies illegally, (g) sent sexually explicit images to someone online or through text messaging, and (h) received sexually explicit images from someone online or through text messaging. A measure of the respondent’s mean online deviance was created (Cronbach’s alpha = .68).

Control Variables

Demographics. Research has consistently identified certain demographic characteristics as potentially important correlates of stalking victimization. For example, with their discussion of the National Violence Against Women Survey, Tjaden and Thoennes (1998) reported that females were more than three times as likely as males to be victims of stalking. Furthermore, Basile, Swahn, Chen, and Saltzman (2006) found that younger individuals had much higher odds of experiencing stalking victimization than older individuals. As has been the case in previous research, the effects of race (White/non-White) and age (in years) will be controlled for in the current analysis.

Offline risky activities. Participation in what are considered risky activities (e.g., drinking heavily, frequently attending parties) has been identified as a well-known correlate of college student victimization (e.g., Fisher et al., 1998; Mustaine & Tewksbury, 1998). However, the relationship between these types of activities and online victimization has thus far not been explored empirically by researchers. Therefore, a measure of risky offline activities was created as a control variable in the current study based on the summed responses to three survey items measuring the respondent’s (a) alcohol consumption, (b) frequency of party attendance, and (c) bar and night club patronage (Cronbach’s alpha = .67).

ANALYSIS

Given the dichotomous nature of the dependent variables, binary logistic regression is an appropriate statistical technique for examining the relationships between online lifestyles and routine activities and cyberstalking victimization. Logistic regression models were estimated for each of the four types of pursuit behaviors constituting cyberstalking victimization—unwanted contact, harassment, sexual advances, and threats of violence—as well as for the overall cyberstalking victimization measure.

Prior to modeling these relationships, the possibility of multicollinearity between the independent variables was explored. The resulting tolerance statistics indicate that multicollinearity is not a statistical issue with the independent variables used in this analysis.2 Relationships are considered statistically significant at the .05 alpha level of significance.

RESULTS

ONLINE EXPOSURE

As Table 2 illustrates, the online exposure variables did not produce consistent effects across the types of pursuit behaviors. However, four of these variables are associated with



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statistically significant increases in likelihood of victimization. For instance, the number of photos posted on an online social network is a significant and positive predictor of online harassment. The number of daily social network updates is associated with a modest increase in odds of receiving unwanted sexual advances while online, and the number of social networking accounts that a respondent has opened as well as the use of AOL Instant Messenger are predictive of increased likelihood of cyberstalking victimization. None of the other online exposure variables produced statistically significant effects on odds of victimization.

ONLINE PROXIMITY

As reported in Table 2, of the three online proximity variables, only one is significantly related to victimization. That is, allowing strangers to access personal online information (i.e., adding strangers as friends to online social networks) is predictive of unwanted contact, harassment, sexual advances, and overall cyberstalking victimization. In each model, the odds of victimization are more than doubled for those who allow strangers to access their online profiles.

ONLINE GUARDIANSHIP

According to Table 2, two of the three online guardianship measures—use of an online profile tracker and online peer deviance—are positive predictors of victimization. Online profile trackers are designed to monitor social network activity so that the user can keep an eye on who is viewing his or her personal information and take preventive measures if troubling patterns develop. This is essentially a form of self-guardianship, but the variable performs contrary to expectations, with those using profile trackers having increased odds of victimization for unwanted contact, threats, and cyberstalking. A possible explanation for this effect is that those who experienced problems online decided to adopt profile trackers to keep themselves safe in the future. The second variable, online peer deviance, increased likelihood of victimization for four of the five victimization variables. Those students who believed that their online friends might harass, threaten, or stalk them using the information that they have posted online are indeed more likely to have such experiences. Although the effects are modest, those with deviant peers online (indicating a lack of capable guardianship) are more likely to experience unwanted contact, harassment, sexual advances, and overall cyberstalking while online.

ONLINE TARGET ATTRACTIVENESS

As Table 2 indicates, gender and relationship status are both significant predictors of online pursuit, whereas the composite target attractiveness measure and sexual orientation are not significantly related to any of the five cyberstalking victimization variables. Consistent with prior victimization research, being female doubles victimization risk for both unwanted contact and harassment victimization, triples the risk for sexual advances, and increases overall cyberstalking victimization odds 1.8 times. Table A1 in the appendix examines males and females separately and suggests that there are few differences in risk factors for cyberstalking victimization across gender. Nonsingles are 1.5 times more likely to experience unwanted communications (i.e., contact) while online compared to singles.




ONLINE DEVIANCE

The online deviance measure stands out as the strongest and most consistent predictor of cyberstalking victimization across models. Table 2 illustrates that higher mean online deviance increases odds of unwanted contact more than 6 times, harassment more than 10 times, sexual advances more than 15 times, threats more than 12 times, and overall cyberstalking more than 14 times. Therefore, consistent with previous work examining the link between offending and victimization, these results not only highlight the link between deviance and victimization but also demonstrate support for this relationship in online environments.

DISCUSSION AND CONCLUSIONS

The results of the current study provide support for the ability of the cyberlifestyle–routine activities theory to explain cyberstalking victimization. Although measures of each theoretical concept were significantly related to various forms of cyberstalking victimization (e.g., repeated contact, unwanted sexual advances), the strength of these relationships varied across the dependent variables (i.e., pursuit behaviors). Online exposure and proximity proved to have the weakest relationships with victimization, with measures being significantly related to only a couple of measures of cyberstalking. Online target attractiveness and guardianship had moderate effects on cyberstalking, with at least one measure of each concept being significantly related to every form of victimization. Finally, online deviance had the strongest effect on all forms of victimization, being significantly related to every form of cyberstalking victimization.

A few of these variable effects warrant further discussion. First, online exposure to offenders—which was shown by Marcum (2009) to have a significant effect on online victimization—proved to be one of the weakest predictors of cyberstalking in the current study. Although this variation could be the result of different methods of operationalization, it is also possible that the introduction of online proximity and target attractiveness measures mediated some of the effect of online exposure. Second, there is a significant and positive relationship between one of the measures of guardianship—use of a profile tracker—and several measures of cyberstalking victimization. On its surface, this finding seems to indicate that self-protective measures can be harmful. As previously mentioned, however, this may be the result of a temporal order issue (i.e., individuals may use the profile tracker after experiencing online victimization). Further research is needed to specify the temporal ordering of this relationship. Third, the composite measure of target attractiveness was not significantly related to any of the cyberstalking pursuit behaviors. This is counter to expectations, as a more attractive target is expected to increase one’s chances of victimization. As reported in Table A2 in the Appendix, however, before controlling for the other theoretical concepts, target attractiveness does indeed increase victimization. Consequently, it has not been clearly established in the existing lifestyle–routine activities literature which (if any) of these theoretical concepts is more important in explaining victimization, and as a result, these measures should not be considered separately. Does guardianship matter more than target attractiveness? Does proximity matter more than exposure? In the current study, the answers to both questions appear to




be yes, but clearly more research is needed to clarify the role of these theoretical concepts in different types of victimization.

Taken as a whole, the results of the current study suggest that the lifestyle–routine activity perspective can be successfully adapted to cyberspace to explain online forms of victimization. In this case, the cyberstalking victimization of college students appears to be a function of decisions and online behaviors that facilitate the intersection of the victim and the offender within cyberspace: specifically, behaviors that bring motivated offenders into closer virtual proximity with the victim (i.e., adding strangers as friends), participating in deviant activities while online (i.e., hacking, harassing others), and associating with deviant peers while online (indicating a lack of capable guardianship online).

The results provide support for our proposed adaptation of lifestyle–routine activities theory to cyberspace environments. This said, we fully acknowledge that no study, including the current study, is without limitations, and it is important to recognize this study’s three primary limitations.

The first limitation pertains to the study’s response rate. As discussed previously, not only do web-based surveys have notoriously low rates of participation, but it is also difficult to accurately gauge the true response rate due to concerns surrounding the delivery/receipt of electronic communications. As such, the results presented in the current study should be considered in light of the potentially low response rate, but with an eye toward their utility. The second limitation of the current study is related to the temporal order of the relationships among the variables, specifically that the dependent variables were not temporally bounded, whereas many of the independent variables were. This raises the possibility that the relationships between variables with different time referents do not operate in the manner they appear, as was argued previously in the context of the relationship between use of a profile tracker and victimization. However, it was necessary to utilize an unbounded measure of the dependent variable since victimization is a relatively rare event and variation in the dependent variable is required to conduct analyses. Third, the current study focused exclusively on pursuit behaviors constituting cyberstalking, omitting the role that stalking in physical space may have on online pursuit. It is entirely possible that offline stalking behaviors may influence one’s likelihood of cyberstalking. Indeed, this possible overlap between stalking and cyberstalking is at the heart of the debate surrounding the nature of cyberstalking and warrants further attention from researchers. Future research should address these issues in an effort to develop a clearer image of the relationship between online lifestyles, routine activities, and victimization.

Despite these limitations, we believe that our work is an important step in expanding lifestyle–routine activities theory beyond its current place-based conceptions to cyberspace and thereby improving researchers’ understanding of victimization that takes place in cyberspace environments. In the past two decades, so many of our daily routines have transitioned from the physical to the virtual world. Online activities and communication are now a part of mainstream society, affecting every aspect of social behavior, even crime. As scholars, it is necessary for us to remain at the forefront of theoretical progress. We must be willing to adapt our theories to the ever-changing opportunity structures of crime and




victimization. To that end, we must continue to develop new and revise old victimization theories. Theories cannot continue to develop and explain our reality if they are restricted by old ideas and static concepts and measures. With that in mind, the current study is our attempt to adapt and apply one of the most widely examined theories of victimization—lifestyle–routine activities theory—to victimization in the cyberworld and develop measures that capture the interaction between victims and offenders in this virtual environment that is part of the social reality for such a large number of people globally.

APPENDIX

TABLE A1: Binary Logistic Regression for Cyberstalking Victimization, by Gender

Model A: Males Model B: FemalesTest of Equality of

Regression Coefficients

Variable Coefficient SE Coefficient SE z-score

Exposure Time online 0.01 0.05 –0.03 0.04 0.70 Number social networks 0.16* 0.07 0.13 0.08 0.25 Number social network updates –0.02 0.03 0.08** 0.03 –2.15* Photos on social network (ln) 0.18 0.12 0.00 0.09 1.21 AOL Instant Messenger 0.12 0.28 0.42* 0.21 –0.84Proximity Add stranger 1.48*** 0.40 0.79*** 0.22 1.54 Friends on social network (ln) –0.30 0.20 0.09 0.15 –1.57 Friend service –0.65 0.70 0.70 0.59 –1.48Guardianship Social network private 0.26 0.31 0.34 0.30 –0.17 Profile tracker 0.44 0.52 0.63* 0.28 –0.32 Deviant peers 0.23*** 0.09 0.30*** 0.07 –0.65Target attractiveness Composite measure 0.21 0.77 0.36 0.47 –0.17 Relationship status –0.14 0.27 0.28 0.20 –1.26 Sexual orientation 0.53 0.57 0.22 0.41 0.43Online deviance 3.84** 0.94 2.09** 0.67 1.51Controls Age 0.16 0.28 –0.26 0.20 1.23 Non-White 0.31 0.45 0.18 0.30 0.25 Offline risky activities –0.01 0.01 0.00 0.01 –0.78 Constant –3.10** 1.09 –3.54*** 0.83 0.32–2 log likelihood 366.42

73.77***0.27380

688.74126.19***

0.26592

Model χ2

Nagelkerke R2

n

*p < .05. **p < .01. ***p < .001.




Table A2: Binary Logistic Regression for Cyberstalking Victimization, by Type of Lifestyle–Routine Activities Theory Component

Variable Coefficient SE Exp(B) –2 Log Likelihood Model χ2 Nagelkerke R2 n

Exposure Time online –0.01 0.03 0.99 1203.53 71.53*** .10 974 Number social networks 0.25*** 0.05 1.29 Number social network updates 0.05* 0.02 1.05 Photos on social network (ln) 0.16** 0.05 1.17 AOL Instant Messenger 0.41** 0.15 1.51 Constant –2.01*** 0.32 0.13Proximity Add stranger 1.00*** 0.17 2.73 1237.15 79.65*** .11 974 Friends on social network (ln) 0.16* 0.08 1.18 Friend service 1.08*** 0.22 2.94 Constant –2.19*** 0.50 0.11Guardianship Social network private 0.22 0.18 1.25 1237.70 79.09*** .11 974 Profile tracker 1.10*** 0.22 3.01 Deviant peers 0.30*** 0.05 1.36 Constant –1.32*** 0.19 0.27Target attractiveness Composite measure 1.33*** 0.30 3.79 1268.70 48.09*** .07 974 Gender 0.60*** 0.14 1.82 Relationship status 0.19 0.14 1.21 Sexual orientation 0.73** 0.28 2.07 Constant –1.69*** 0.23 0.19Online deviance 3.41*** 0.48 30.38 1258.94 57.85*** .08 974 Constant –1.03*** 0.11 0.36

*p < .05. **p < .01. ***p < .001.

NOTES

1. By conventional standards, a response rate of 13.1% is low, but recent research suggests that it is not surprising given the mode of administration and the population under study. For instance, Dillman et al. (2009) compared response rates of mixed-mode surveys and reported that response rates varied widely across modes of administration. In this study, mail surveys produced the highest response rates at 75%, and web surveys yielded the lowest at 12.7%. Similar studies of comparable populations have produced rates of participation similar to those in the current study (e.g., Hilinski, 2009; Nobles, Fox, Piquero, & Piquero, 2009; Patton, Nobles, & Fox, 2010).

2. According to Menard (2010), tolerance statistic values less than 0.2 are cause to be concerned about multicollinearity among the independent variables. In the current study, tolerance was estimated for these variables, and the values for these statistics range between 0.6 and 0.9. These are well within the acceptable range, indicating that multicollinearity is not a statistical threat to the results of the current study.

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Bradford W. Reyns is an assistant professor in the Department of Criminal Justice at Weber State University and the book review editor for Security Journal. In 2010, Reyns received his PhD in criminal justice from the University of Cincinnati. His research focuses on victims of crime, especially the intersection of technology and victimization, and opportunities for victimization. Recently, his work has appeared in Deviant Behavior, Journal of Criminal Justice, and Violence and Victims.

Billy Henson is an assistant professor in the Department of Criminal Justice at Shippensburg University. His previous works have focused on crime prevention, interpersonal victimization, fear of crime, and policing, with studies appearing in Police Quarterly, Victims and Offenders, Deviant Behavior, Violence and Victims, and Youth Violence and Juvenile Justice. He continues to perform research on violent, sexual, and repeat victimization; fear of crime; and online victimization.

Bonnie S. Fisher is a professor in the School of Criminal Justice and a Fellow of the Graduate School at the University of Cincinnati. She coedited the Encyclopedia of Victimology and Crime Prevention (Sage). She coauthored Unsafe in the Ivory Tower: The Sexual Victimization of College Women (Sage) and The Dark Side of the Ivory Tower: Campus Crime as a Social Problem (Cambridge University Press). She continues to pursue her research agenda examining various issues underlying the victimization of college students. Professor Fisher is collaborating on a multiple-campus evaluation of bystanding intervention programs directed at reducing sexual and dating violence on college campuses.



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